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| Deep Graph Convolutional Network with Dual-Branch and Multi-interaction |
| LOU Jiaqi1, YE Hailiang1, YANG Bing1, LI Ming2, CAO Feilong1 |
1. Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018;
2. Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004 |
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Abstract Graph neural networks show excellent performance in node classification tasks. However, how to fully obtain high-order semantic features of graph data and prevent over-smoothing is one of the key issues affecting the accuracy of node classification. Therefore, deep graph convolutional network with dual-branch and multi-interaction is constructed to enhance the ability to acquire high-order semantic features of nodes. Firstly, the graph structure is reconstructed according to the feature information of the nodes. Then, a dual-branch network architecture is established by both the original and the constructed graph structures to fully extract different high-order semantic features. A channel information interaction mechanism is designed to increase the diversity of node features by learning the information interaction of different branches. Finally, experiments on multiple benchmark datasets demonstrate that the proposed method improves the accuracies of the semi-supervised node classification tasks and alleviates the over-smoothing phenomenon effectively.
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Received: 23 May 2022
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| Fund:National Natural Science Foundation of China(No.62006215,62176244,62172370), Natural Science Foundation of Zhejiang Province(No.LQ20F030016) |
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Corresponding Authors:
YE Hailiang, Ph.D., lecturer. His research interests include deep learning and its application, graph neural networks, machine learning and its application.
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About author:: LOU Jiaqi, master student. His research interests include deep learning and graph neural networks.
YANG Bing, Ph.D., lecturer. His research interests include deep learning and image processing.
LI Ming, Ph.D., professor. His research interests include deep learning and graph neural networks.
CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing. |
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2022, Vol. 35 
